PDF Query Assistant with OCR

PDF Query Assistant is an intelligent web application that allows users to extract information from PDF documents using natural language queries. It supports both text-based and scanned PDFs through OCR technology, powered by the Mistral AI model via Ollama.

🚀 Features

• 📄 PDF Upload & Processing: Upload PDF documents via drag-and-drop or file browser.

• 🔍 Dual Extraction Methods

  • Text extraction from standard PDFs using PyMuPDF.
  • OCR processing for scanned PDFs using Tesseract.

• 🤖 AI-Powered Querying: Ask natural language questions about your PDF content using the Mistral model.

• 📱 Responsive Design: Clean, professional interface with dark mode support.

• ⚡ Real-time Processing: Instant responses to your PDF queries.

• 📊 Content Analysis: Extract insights, summaries, and specific information from documents.

🛠️ Technology Stack

• Backend: FastAPI (Python)
• AI Processing: Ollama with Mistral model
• PDF Processing: PyMuPDF (fitz)
• OCR Engine: Tesseract OCR
• Frontend: HTML5, CSS3, JavaScript
• Deployment: Uvicorn ASGI server

🛠️ Installation & Setup

Clone the Repository

git clone https://github.com/Yuvakunaal/ChatWithPDF-AI.git
cd ChatWithPDF-AI

Install Python Dependencies

pip install fastapi uvicorn pymupdf ollama pydantic python-multipart pillow pytesseract

Install Tesseract OCR

• Windows: Download from UB-Mannheim/tesseract
• macOS: bash brew install tesseract
• Linux (Ubuntu/Debian): bash sudo apt install tesseract-ocr
• Linux (Fedora): bash sudo dnf install tesseract

Download Ollama

• Visit the Ollama website and download the application.
• Open your terminal and run:

  ollama pull mistral:instruct

• The Ollama model is now downloaded on your system.

Run the Application

uvicorn app:app --reload

Open Browser : Navigate to http://127.0.0.1:8000/

🎯 How It Works

Upload PDF: Drag and drop or select a PDF file.

Automatic Processing:

• The system first attempts standard text extraction.
• If text content is minimal (scanned PDF), it automatically uses OCR.

Ask Questions: Type natural language questions about the document.

Get Answers: AI analyzes the PDF content and provides accurate responses.

🔮 Usage Examples

Ask questions like:

• "What is this document about?"
• "Summarize the main points."
• "List all the key findings."
• "What are the recommendations in section 3?"
• "Extract all dates mentioned in the document."
• "Who are the authors of this paper?"

The application analyzes the PDF content and provides precise answers based solely on the document information.

🌟 Advanced Features

• 🤖 Intelligent Context Handling: AI understands document structure and references specific sections.
• 📄 Scanned PDF Support: Automatic OCR processing for image-based PDFs.
• 🔒 Privacy-Focused: All processing happens locally on your machine.
• ⚡ Performance Optimized: Efficient text extraction and query processing.
• 📱 Mobile Responsive: Works seamlessly on desktop and mobile devices.

👨‍💻 Developer

Kunaal – AI, Python, and Web Development Enthusiast

🙏 Acknowledgments

• Ollama for enabling local AI model integration.
• FastAPI for providing a high-performance backend framework.
• PyMuPDF and Tesseract for PDF processing capabilities.
• The open-source community for the libraries and tools used.

⭐ If you find this project useful, please consider starring the repository!